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  • 1.
    Leturiondo, Urko
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. IK4-Ikerlan.
    Mishra, Madhav
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Salgado, Oscar
    IK4-Ikerlan.
    Synthetic data generation in hybrid modelling of rolling element bearings2015Inngår i: Insight (Northampton), ISSN 1354-2575, E-ISSN 1754-4904, Vol. 57, nr 7, s. 395-400Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Diagnosis and prognosis processes are necessary to optimise the dependability of systems and ensure their safe operation. If there is a lack of information, faulty conditions cannot be identified and undesired events cannot be predicted. It is essential to predict such events and mitigate risks, but this is difficult in complex systems.Abnormal or unknown faults cause problems for maintenance decision makers. We therefore propose a methodology that fuses data-driven and model-based approaches. Real data acquired from a real system and synthetic data generated from a physical model can be used together to perform diagnosis and prognosis.As systems have time-varying conditions related to both the operating condi- tions and the healthy or faulty state of systems, the idea behind the proposed methodology is to generate synthetic data in the whole range of conditions in which a system can work. Thus, data related to the context in which the system is operating can be generated.We also take a first step towards implementing this methodology in the field of rolling element bearings. Synthetic data are generated using a physical model that reproduces the dynamics of these machine elements. Condition indicators such as root mean square, kurtosis and shape factor, among others, are calculated from the vibrational response of a bearing and merged with the real features obtained from the data collected from the functioning systemFinally, the merged indicators are used to train SVM classifiers (support vector machines), so that a classification according to the condition of the bearing is made independently of the applied loading conditions even though some of the scenarios have not yet occurred.

  • 2.
    Leturiondo, Urko
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. IK4-Ikerlan.
    Mishra, Madhav
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Salgado, Oscar
    IK4-Ikerlan.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Modelo dinámico de rodamientos para su estudio frente a fallos geométricos locales2014Konferansepaper (Fagfellevurdert)
  • 3.
    Leturiondo, Urko
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. IK4-Ikerlan.
    Mishra, Madhav
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Salgado, Oscar
    IK4-Ikerlan.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Nonlinear response of rolling element bearings with local defects2014Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Rolling element bearings have been studied for decades, but more research is required into their dynamics, especially failure due to different kinds of damage in the context of condition monitoring. The appearance of a failure in an element of a bearing, as well as its degradation, can entail not only a malfunction in the system in which it is located, but also a catastrophic failure. This work presents a multi-body model of a rolling element bearing with the objective of analysing the dynamics of the bearing and emphasising the effect of defects in any of its element. The study models the metal-metal contacts between the bearing’s elements using the Hertz contact and the elastohydrodynamic lubricationtheories, both of which are theories of nonlinearity. It also considers the non-stationary regime of bearings and local geometric damage. Its results are compared with results in the literature. Finally, it includes a set of additional results showing different aspects of the response of the bearing.

  • 4.
    Leturiondo, Urko
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. IK4-Ikerlan.
    Salgado, Oscar
    IK4-Ikerlan.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Mishra, Madhav
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Methodology for the Estimation of the Fatigue Life of Rolling Element Bearings in Non-stationary Conditions2016Inngår i: Advances in Condition Monitoring of Machinery in Non-Stationary Operations: Proceedings of the Fourth International Conference on Condition Monitoring of Machinery in Non-Stationary Operations, CMMNO'2014, Lyon, France December 15-17 / [ed] Fakher Chaari; Radozlaw Zimroz; Walter Bertelmus; Mahamed Haddar, Cham: Encyclopedia of Global Archaeology/Springer Verlag, 2016, Vol. 4, s. 413-423Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The estimation of the life of rolling element bearings (REBs) is crucial to determine when maintenance is required. This paper presents a methodology to calculate the fatigue life of REBs considering non-stationary conditions. Instead of taking a constant value, the paper considers cyclic loading and unloading processes, as well as increasing and decreasing values of the speed of rotation. It employs a model-based approach to calculate contact loads between the different elements of the bearing, with a finite element model (FEM) used to calculate the contact stresses. Using this information, it then performs a fatigue analysis to study overloading in faulty bearings.

  • 5.
    Mishra, Madhav
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Hybrid Models for Rotating Machinery Diagnosis and Prognosis2015Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The research hypothesis is that fusing the output of more than one method will improve the accuracy and precision of the remaining useful life estimation.

  • 6.
    Mishra, Madhav
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Model-Based Prognostic Approach for Battery Variable Loading Conditions: Some Accuracy Improved2017Inngår i: Proceedings of the Asia Pacific Conference of  the Prognostics and Health Management Society 2017, 2017, s. 147-149Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Prognostics and Health Management (PHM) using a proper condition-based maintenance (CBM) deployment is a worldwide-accepted strategy and has grown very popular in many industries and academia over the past decades. PHM can provide a state assessment of the future health of systems or components, e.g. when a degraded state has been found. Using this technology, one can estimate how long it will take before the equipment will reach a failure threshold, in future operating conditions and future environmental conditions.

    This paper deals with the improvement of prognostic accuracy for battery discharge prediction and compares with previous results done by the other researchers. In this paper, physical models and measurement data were used in the prognostic development in such a way that the degradation behaviour of the battery could be modelled and simulated in order to predict the end-of-discharge (EoD). A particle filter turned out to be the method of choice in performing the state assessment and predicting the future degradation. 

  • 7.
    Mishra, Madhav
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Model-based Prognostics for Prediction of Remaining Useful Life2015Licentiatavhandling, med artikler (Annet vitenskapelig)
    Abstract [en]

    Prognostics and healthmanagement (PHM) is an engineering discipline that aims to maintain the systembehaviour and function, and assure the mission success, safety andeffectiveness. Health management using a proper condition-based maintenance (CBM)deployment is a worldwide accepted technique and has grown very popular in manyindustries over the past decades. These techniques are relevant in environmentswhere the prediction of a failure and the prevention and mitigation of itsconsequences increase the profit and safety of the facilities concerned.Prognosis is the most critical part of this process and is nowadays recognizedas a key feature in maintenance strategies, since estimation of the remaininguseful life (RUL) is essential.PHM can provide a stateassessment of the future health of systems or components, e.g. when a degradedstate has been found. Using this technology, one can estimate how long it willtake before the equipment will reach a failure threshold, in future operatingconditions and future environmental conditions. This thesis focuses especiallyon physics-based prognostic approaches, which depend on a fundamentalunderstanding of the physical system in order to develop condition monitoringtechniques and to predict the RUL.The overall research objective of thework performed for this thesis has been to improve the accuracy and precisionof RUL predictions. The research hypothesis is that fusing the output of morethan one method will improve the accuracy and precision of the RUL estimation,by developing a new approach to prognostics that combines different remaininglife estimators and physics-based and data-driven methods. There are two waysof acquiring data for data-driven models, namely measurements of real systemsand syntactic data generation from simulations. The thesis deals with two casestudies, the first of which concerns the generation of synthetic data andindirect measurement of dynamic bearing loads and was performed atBillerudKorsäs paper mill at Karlsborg in Sweden. In this study the behaviourof a roller in a paper machine was analysed using the finite element method(FEM). The FEM model is a step towards the possibility of generating syntheticdata on different failure modes, and the possibility of estimating crucialparameters like dynamic bearing forces by combining real vibration measurementswith the FEM model. The second case study deals with the development ofprognostic methods for battery discharge estimation for Mars-based rovers. Herephysical models and measurement data were used in the prognostic development insuch a way that the degradation behaviour of the battery could be modelled andsimulated in order to predict the life-length. A particle filter turned out tobe the method of choice in performing the state assessment and predicting thefuture degradation. The method was then applied to a case study of batteriesthat provide power to the rover.

  • 8.
    Mishra, Madhav
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Prognostics and Health Management of Engineering Systems for Operation and Maintenance Optimisation2018Doktoravhandling, med artikler (Annet vitenskapelig)
    Abstract [en]

    Prognostics and health management (PHM) is an engineering discipline that aims to maintain system behaviour and function and ensure mission success, safety and effectiveness. Prognostics is defined as the estimation of remaining useful life. It is the most critical part of this process and is a key feature of maintenance strategies since the estimation of the remaining useful life (RUL) is essential to avoiding unscheduled maintenance. Prognostics is relatively immature compared to diagnostics, and a challenging task facing the research community is to overcome some of the major barriers to the application of PHM technologies to real-world industrial systems. This thesis presents research into methods for addressing these challenges for industrial applications. The thesis work focuses on prognostic approaches for three different engineering systems with different characteristics in terms of the prognostics of operation and maintenance aspects. The aim of this thesis is to facilitate better operation and maintenance decision making. The main benefits of prognostics are in anticipating future failures to increase uptime, implementing dynamic maintenance planning toward decreasing total costs and decreasing energy consumption. Therefore, there is a need for methods that can be used in these cases to classify the health states and predict the remaining useful life of assets. The studied engineered systems in this thesis are railway tracks, batteries and rolling element bearings.

    In a railway system, the track geometry has to be maintained to provide a safe and functional track. Therefore, track degradation of ballasted railway track systems has to be measured on a regular basis to determine when to maintain the track by tamping. Tamping aims to restore the geometry to its original state to ensure an efficient, comfortable and safe transportation system. To minimise the disruption introduced by tamping, this action has to be planned in advance. Track degradation forecasts derived from regression methods are used to predict when the standard deviation of a specific track section will exceed a predefined maintenance or safety limit. In this thesis, a particle-filter-based prognostic approach for railway track degradation for railway switches is proposed. The particle-filter-based prognostic will generate a probabilistic prediction result that can facilitate risk-based decision making.

    Li-ion batteries are another important components in engineering system and battery life prediction matters. Li-ion batteries are commonly used in a wide range of consumer electronic devices, electric vehicles of all types, military electronics,  maritime applications, astronaut suits, and space systems. Many critical operations depend on such batteries as a reliable power source. It is therefore important for the user to get an accurate estimate of the battery end of discharge because an unforeseen discharge of a battery could have catastrophic consequences. To address this issue, a Bayesian hierarchical model (BHM)-based prognostics approach was applied to Li-ion batteries, where the goal was to analyse and predict the discharge behaviour of such batteries with variable load profiles and variable amounts of available discharge data. The BHM approach enables inferences for both individual batteries and groups of batteries. Estimates of the hierarchical model parameters and the individual battery parameters are presented, and dependencies on load cycles are inferred. The operational and reliability aspects, end of life (EoD) and end of life (EoL), are studied; it is shown that predictions of the EoD can be made accurately with a variable amount of battery data. Without access to measurements, e.g., predicting performance of a new battery, the predictions are based only on the prior distributions describing the similarity within a group of batteries and their dependency on the load cycle. A discharge cycle dependency is identified helping with estimation of battery reliability.

    Batteries have become a very important engineering system, rotating machines have played an important role, possibly the most important role, in the field of engineering. They have been used to drive the industrialisation of the world.

    For rotating machinery, rolling element bearings are a vital component and have several failure modes. Hence, there is  significant need to monitor the health of bearings and detect degraded  states and  upcoming  failures  as  early  as  possible  to avoid serious accidents and equipment failure. For  rolling element bearings, an investigation in using FEM models for estimating bearing forces from acceleration measurements was conducted. This study was performed at a paper mill where a bearing monitoring system was installed. The purpose of the study was to feed the bearing rating life L10 (a bearing life length calculation) with estimations of the dynamic bearing forces  to continuously update the L10 calculation by generating a dynamic L10. In a second study for bearing lifetime prediction, a Bayesian hierarchical modelling (BHM) approach , which includes different data sources, such as enveloped acceleration data, in combination with degradation models and prior distributions of other parameters, was developed, in which the bearing rating life calculation can be included. The proposed prognostics methodology can be used in cases where there is less  or noisy data. The above approach can even be used in cases whereby there is no prior knowledge of the system or little measurement data on the conditions. The presented BHM approach can also be used to predict the remaining useful life (RUL) of bearings both in situations in which the bearing is considered to be in a healthy state and in situations after a defect has been detected.

  • 9.
    Mishra, Madhav
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Leturiondo, Urko
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Synthetic data for hybrid prognosis2014Inngår i: Proceedings of the European Conference of the Prognostics and Health Management Society 2014, 2014, s. 796-801Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Using condition-based maintenance (CBM) to assess machinery health is a popular technique in many industries, especially those using rotating machines. CBM is relevant in environments where the prediction of a failure and the prevention and mitigation of its consequences increase both profit and safety. Prognosis is the most critical part of this process and the estimation of Remaining Useful Life (RUL) is essential once failure is identified. This paper presents a method of synthetic data generation for hybrid model-based prognosis. In this approach, physical and data-driven models are combined to relate process features to damage accumulation in time-varying service equipment. It uses parametric models and observer-based approaches to Fault Detection and Identification (FDI). A nominal set of parameters is chosen for the simulated system, and a sensitivity analysis is performed using a general-purpose simulation package. Synthetic data sets are then generated to compensate for information missing in the acquired data sets. Information fusion techniques areproposed to merge real and synthetic data to create training data sets which reproduce all identified failure modes, even those that do not occur in the asset, such as Reliability Centered Maintenance (RCM), Failure Mode and Effect Analysis(FMEA). This new technology can lead to better prediction of remaining useful life of rotating machinery and minimizing and mitigating the costly effects of unplanned maintenance actions.

  • 10.
    Mishra, Madhav
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Leturiondo, Urko
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Salgado, Oscar
    IK4-Ikerlan.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Hybrid modelling for failure diagnosis and prognosis in the transport sector: Acquired data and synthetic data2015Inngår i: Dyna, ISSN 0012-7361, Vol. 90, nr 2, s. 139-145Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Safety in transport is a key. Railway and aerospace sectors have a need for ways to predict the behaviour of trains and aircraft, respectively. With this information, maintenance tasks for the correct operation of the assets can be carried out, reducing the number of failures that can cause an accident. However, the lack of enough data of the faulty state of those systems makes this to be difficult. Because of that either hidden faults or unknown faults can occur. As regulations in transport are very restrictive, components are usually substituted in early states of their degradation, which implies a loss of useful life of those components.In this article a methodology to overcome this limitation is presented. This methodology consists in the fusion of data obtained from two sources: data acquired from the real system, and synthetic data generated using physical models of the system. These physical models should be constructed in such a way that they can reproduce the main failure modes that can occur in the modelled system. This data fusion, that creates a hybrid model, not only allows to classify the condition of the system according to the aforementioned failure modes, but also to define new data that do not belong to any of those failure modes as a new failure mode, improving diagnosis and prognosis processes.

  • 11.
    Mishra, Madhav
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser.
    Martinsson, Jesper
    Luleå tekniska universitet, Institutionen för teknikvetenskap och matematik.
    Goebel, Kai
    NASA Ames Research Center, Intelligent Systems Division, Moffett Field, CA. USA.
    Rantatalo, Matti
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser.
    Bearing life prediction with informed hyper-prior distribution: A Bayesian hierarchical approachInngår i: IEEE/ASME transactions on mechatronics, ISSN 1083-4435, E-ISSN 1941-014XArtikkel i tidsskrift (Fagfellevurdert)
  • 12.
    Mishra, Madhav
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Martinsson, Jesper
    Luleå tekniska universitet, Institutionen för teknikvetenskap och matematik, Matematiska vetenskaper.
    Rantatalo, Matti
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Goebel, Kai
    NASA Ames Research Center, Intelligent Systems Division, Moffett Field, CA.
    Bayesian hierarchical model-based prognostics for lithium-ion batteries2018Inngår i: Reliability Engineering & System Safety, ISSN 0951-8320, E-ISSN 1879-0836, Vol. 172, s. 25-35Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    To optimise operation and maintenance, knowledge of the ability to perform the required functions is vital. The ability is governed by the usage of the system (operational issues) and availability aspects like reliability of different components. This paper proposes a Bayesian hierarchical model (BHM)-based prognostics approach applied to Li-ion batteries, where the goal is to analyse and predict the discharge behaviour of such batteries with variable load profiles and variable amounts of available discharge data. The BHM approach enables inferences for both individual batteries and groups of batteries. Estimates of the hierarchical model parameters and the individual battery parameters are presented, and dependencies on load cycles are inferred. A BHM approach where the operational and reliability aspects end of life (EoD) and end of life (EoL) is studied where its shown that predictions of EoD can be made accurately with a variable amount of battery data. Without access to measurements, e.g. predicting a new battery, the predictions are based only on the prior distributions describing the similarity within the group of batteries and their dependency on the load cycle. A discharge cycle dependency can also be identified in the result giving the opportunity to predict the battery reliability.

  • 13.
    Mishra, Madhav
    et al.
    Division of Operation and Maintenance Engineering within the framework of the SKF-University of Technology Centre (UTC).
    Martinsson, Jesper
    Luleå tekniska universitet, Institutionen för teknikvetenskap och matematik, Matematiska vetenskaper. Luossavaara-Kiirunavaara AB.
    Rantatalo, Matti
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Goebel, Kai
    NASA Ames Research Center, Intelligent Systems Division, Moffett Field, CA, USA.
    Hierarchical model-based prognostics for Li-ion batteries2018Inngår i: Advances In ENGINEERINGArtikkel i tidsskrift (Annet (populærvitenskap, debatt, mm))
    Abstract [en]

    Recent global trend towards a fossil-fuel-free society has yielded the rapid soar in demand of electrically powered systems. Specifically, the demand for battery powered systems has fueled the desire to have better performing batteries with lithium-ion batteries being the most widely used. Presently, for application in unmanned vehicles, exploratory rovers, submarines among others demand a better comprehension of battery performance metrics. Case and point, battery capacity and state of charge have become increasingly vital when it comes to determining the end of discharge. As of now, several techniques have already been established for determining such parameters. Unfortunately, their prognostic capability for determining remaining battery charge is still not optimal. Therefore, there is a need to develop prognostic and health management technology for critical systems (such as Mars rovers) to successfully predict and manage the lifetime of batteries, monitor their health state in real time, evaluate the performance and predict the remaining useful life.

    To this note, Luleå University of Technology researchers in Sweden: Dr. Madhav Mishra, Dr. Jesper Martinsson, and Dr. Matti Rantatalo in collaboration with Dr. Kai Goebel at NASA in the United States proposed a study whose main objective was to measure the battery discharge and predict the end of discharge considering the operating conditions for lithium ion batteries. To be precise, they purposed on employing a Bayesian Hierarchical Model (BHM)-based end of discharge prognostic for Li-ion batteries. Their work is currently published in the research journal, Reliability Engineering and System Safety.

    The research technique employed entailed the utilization of two batteries with 16 discharge events with a simplified battery circuit model of the battery. Next, the research team examined the detailed discharge voltage profiles during different discharging cycles with variable load profiles. They then proceeded to demonstrate the BHM approach and group-level dependencies by utilizing more than one battery and more than one discharge cycle.

    The authors observed that the BHM approach enabled inferences for both individual batteries and groups of batteries. The researchers then recorded the estimates of the hierarchical model parameters and the individual battery parameters after which their dependencies on load cycles were inferred. In addition, they noted that by using the BHM approach the predictions of end of discharge could be made accurately with a variable amount of battery data. Furthermore, this technique was seen to applicable even for new batteries without prior recorded data where the predictions were based only on the prior distributions describing the similarity within the group of batteries and their dependency on the load cycle.

    In conclusion, the study presented a Bayesian hierarchical model (BHM)-based prognostics approach for Li-ion batteries, where the goal was to analyze and predict the discharge behavior of such batteries with variable load profiles and variable amounts of available discharge data. The results obtained showed that the technique could address cases with or without data. Altogether, the proposed method can capture additional relationships between parameters and use it to improve prognostics. Lastly, the BHM approach has been seen to permit inference at both the individual battery level and group of battery level.

  • 14.
    Mishra, Madhav
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Odelius, Johan
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Rantatalo, Matti
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Johnsson, Roger
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Larsson, Jan-Olof
    SKF Sweden.
    Bellander, Magnus
    SKF Sweden.
    Niemi, Ingemar
    Billerud Karlsborg AB.
    Simulations and Measurements of the Dynamic Response of a Paper Machine Roller2015Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The paper industry is a highly automated industry that includes many different production steps where a variety of machine components are used. In the paper machine where the pulp is being transformed into paper, rotating components like bearing mounted rollers play an important part to drive the wire with the pulp through the process. In this type of industry with a serial layout, the failure of a single roller or bearing could lead to stoppage of several production steps with costly consequences as a result. To ensure and optimize the asset availability, a condition based maintenance (CBM) strategy could be implemented. However, CBM is dependent on an appropriate condition monitoring (CM) technique to detect physical phenomenon that defines the state of critical components or systems. For the development of CM techniques, it is therefore important to understand and model the physical behaviour of the system in question. In this paper the behaviour of a roller in a paper machine is analysed using finite element method (FEM). The physical model was compared with vibration measurements collected from an online monitoring system and an experimental modal analysis.

  • 15.
    Mishra, Madhav
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Odelius, Johan
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Rantatalo, Matti
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Johnsson, Roger
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Larsson, Jan-Olof
    SKF Sweden.
    Bellander, Magnus
    SKF Sweden.
    Niemi, Ingemar
    Billerud Karlsborg AB.
    Simulations and measurements of the dynamic response of a paper machine roller2016Inngår i: Insight (Northampton), ISSN 1354-2575, E-ISSN 1754-4904, Vol. 58, nr 4, s. 210-212Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The paper industry is a highly automated industry that includes many different production steps, in which a variety of machine components are used. In a paper machine, where the pulp is being transformed into paper, rotating components such as bearing-mounted rollers play an important part in driving the wire with the pulp through the process. In this type of industry with a serial layout, the failure of a single roller or bearing could lead to the stoppage of several production steps, with costly consequences as a result. To ensure and optimise asset availability, a condition-based maintenance (CBM) strategy could be implemented. However, CBM is dependent on an appropriate condition monitoring (CM) technique to detect a physical phenomenon that defines the state of critical components or systems. For the development of CM techniques, it is therefore important to understand and model the physical behaviour of the system in question. In this paper, the behaviour of a roller in a paper machine is analysed using the finite element method (FEM). The physical model was compared with vibration measurements collected from an online monitoring system and an experimental modal analysis.

  • 16.
    Mishra, Madhav
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Odelius, Johan
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Thaduri, Adithya
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Nissen, Arne
    Trafikverket, Luleå.
    Rantatalo, Matti
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Particle filter-based prognostic approach for railway track geometry2017Inngår i: Mechanical systems and signal processing, ISSN 0888-3270, E-ISSN 1096-1216, Vol. 96, s. 226-238Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Track degradation of ballasted railway track systems has to be measured on a regular basis, and these tracks must be maintained by tamping. Tamping aims to restore the geometry to its original shape to ensure an efficient, comfortable and safe transportation system. To minimize the disturbance introduced by tamping, this action has to be planned in advance. Track degradation forecasts derived from regression methods are used to predict when the standard deviation of a specific track section will exceed a predefined maintenance or safety limit. This paper proposes a particle filter-based prognostic approach for railway track degradation; this approach is demonstrated by examining different railway switches. The standard deviation of the longitudinal track degradation is studied, and forecasts of the maintenance limit intersection are derived. The particle filter-based prognostic results are compared with the standard regression method results for four railway switches, and the particle filter method shows similar or better result for the four cases. For longer prediction times, the error of the proposed method is equal to or smaller than that of the regression method. The main advantage of the particle filter-based prognostic approach is its ability to generate a probabilistic result based on input parameters with uncertainties. The distributions of the input parameters propagate through the filter, and the remaining useful life is presented using a particle distribution.

  • 17.
    Mishra, Madhav
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Rantatalo, Matti
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Hybrid Models for Rotating Machinery Prognosis:Estimate Remaining Useful Life2016Konferansepaper (Fagfellevurdert)
  • 18.
    Mishra, Madhav
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Rantatalo, Matti
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Odelius, Johan
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    A Model-based Prognostic Approach to Predict Remaining Useful Life of Components2016Inngår i: Proceedings of 1st International Conference on Maintenance Engineering, IncoME-I, 2016 / [ed] Jyoti K. Sinha, Akilu Yunusa-Kaltungo, Wolfgang Hahn, 2016, artikkel-id ME2016_1147Konferansepaper (Fagfellevurdert)
    Abstract [en]

    One of the major problems in the industry is the extension of the useful life of high-performance systems. Proper maintenance plays an important role by extending the useful life, reducing the lifecycle costs and improving the reliability and availability. Health management using a proper condition-based maintenance (CBM) deployment is a worldwide accepted strategy and has grown very popular in many industries over the past decades. A case of CBM is when the maintenance decision is taken based on a forecast of the asset state. This strategy is called predictive maintenance or prognostic health management (PHM). PHM is an engineering discipline that aims to maintain the system behaviour and function, and assure the mission success, safety and effectiveness. This strategy is relevant in environments where the prediction of a failure and the prevention and mitigation of its consequences increase the profit and safety of the facilities concerned. Prognosis is the most critical part of this process and is nowadays recognized as a key feature in maintenance strategies since estimation of the remaining useful life (RUL) is essential.

    PHM can provide a state assessment of the future health of systems or components, e.g. when a degraded state has been found. The aim of using PHM is to estimate how long it will take before the equipment will reach a failure threshold, in future operating conditions and future environmental conditions.

    The aim of the paper is to improve the estimation of bearing RUL by dynamically updating the SKF L10 bearing life length calculation. Using a physics-based prognostic approach, the behaviour of a roller in a paper machine was simulated using the finite element method (FEM). A transfer function representing the relation between bearing acceleration and bearing forces was generated and used to convert the acceleration signal into an estimation of the dynamically changing bearing force. The estimated force is then used as input to the bearing life length calculation generating an updated L10 calculation for each time step. 

  • 19.
    Mishra, Madhav
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Saari, Juhamatti
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Leturiondo, Urko
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Hybrid Models for Rotating Machinery Diagnosis and Prognosis: Estimation of Remaining Useful Life2014Rapport (Annet vitenskapelig)
    Abstract [en]

    The purpose of this literature review is to summarise the various technologies that can be used for machinery diagnosis and prognosis. The review focuses on Condition Based Maintenance (CBM) in machinery systems, with a short description of the theory behind each technology; it also includes references to state-of-the-art research into each theory. When we compare technologies, especially with respect to cost, complexity, and robustness, we find varied abilities across technologies. The machinery health assessment for CBM deployment is accepted worldwide; it is very popular in industries using rotating machines involved. These techniques are relevant in environments where predicting a failure and preventing or mitigating its consequences will increase both profit and safety. Prognosis is the most critical part of this process and is now recognised as a key feature in maintenance strategies; the estimation of Remaining Useful Life (RUL) is essential when a failure is identified. The literature review identifies three basic ways to model the fault development process: with symbols, data, or mathematical formulations based on physical principles. The review discusses hybrid approaches to machinery diagnosis and prognosis; it notes some typical approaches and discusses their advantages and disadvantages.

  • 20.
    Mishra, Madhav
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Thaduri, Adithya
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Ontology based diagnosis for maintenance decisions of Paper Mill roller using dynamic response2016Inngår i: Current Trends in Reliability, Availability, Maintainability and Safety: An Industry Perspective / [ed] Uday Kumar; Alireza Ahmadi; Ajit Kumar Verma; Prabhakar Varde, Encyclopedia of Global Archaeology/Springer Verlag, 2016, s. 187-196Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Context-aware systems have been applied in several fields like Information Technology, mobile, web services, travel guidance etc. These systems deliver decisions based on a ‘context’ by using contextual models. In paper industries, the failures of rollers were prominent and rolling element bearing is one of the critical components. The failure occurs due to the varying levels of the loads and external parameters that defines context. This paper demonstrates the ontology contextual modeling for the diagnosis of rollers as a context by using dynamic response. The roller is modeled using physical models and applying runs of different parameters and its levels. Then contextual models are generated for rollers to show relation among input contextual parameters with different features. This paper shows that this conceptual idea of decision based on different contexts using ontology models is for effective diagnosis facilitates maintenance strategies and further prospects in prognosis.

  • 21.
    Mishra, Madhav
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    van Riet, Maarten
    Alliander N.V., The Netherlands.
    A channel model for power line communication using 4PSK technology for diagnosis: Some lessons learned2018Inngår i: International Journal of Electrical Power & Energy Systems, ISSN 0142-0615, E-ISSN 1879-3517, Vol. 95, s. 617-634Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Modern smart grids and smart metering concepts are based on reliable digital communications. The absence of dedicated communications media, such as telephone lines or fibre optics within a power line network, can make transmission challenging. Electrical power companies are interested in implementing an overall communicating power line network. The power line communication (PLC) system uses the electric power distribution grid as a data transmission medium. The data transmission problem resulted due to poorly developed Medium Voltage Network of PLC Channel Model and challenges in data transmission technology, so this hampers better performance. This paper studies PLC over a medium voltage network with a goal of achieving greater bit rates and communication that is more reliable over power lines. It presents a complete channel model of a PLC system and evaluation of Bit Error Rate (BER) of Phase Shift Keying (PSK) when corrupted with noise. It calculates the number of sections between two substations to determine signal loss. The PSK modulation scheme in simulation is experimentally found to be more robust against such power line distortions as noise for point-to-point transmission. The model and calculations use Matlab and QUCS.

  • 22.
    Saari, Juhamatti
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Mishra, Madhav
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Johansson, Carl-Anders
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Applied methods of condition monitoring and fault detection for underground mobile machines2013Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Condition monitoring is a health assessment technique worldwide accepted and very popular in many industries especially where there are rotating machines involved in the processes. These techniques may be relevant in environments where the prediction of a failure and the prevention and mitigation of its consequences increase the profit and safety of the facilities. The maintenance of underground mobile mining equipment is one of these scenarios. It has several problem areas: harsh environment, potential risks and distant location of workshops. When a machine breaks down, there are two ways to handle the repair. Either the equipment has to be repaired on site at the production area or taken to the workshop. The difficulties involved in moving this type of large equipment are substantial but it might be difficult or unsafe to repair the LHD on site (depending on where and why it fails). Therefore it is necessary to identify the critical components and monitor them properly in order to skip undesired shutdowns or stoppages. This paper describes the benefits of different CM techniques applied to a critical part of a LHD machine (the transmission) in order to detect the abnormal behavior if any, identify the fault and predict the degradation. These techniques will provide enough information to optimize the maintenance actions minimizing and mitigating the costly effects of unplanned actions.

1 - 22 of 22
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